Methods for biometric recognition based on facial features, including features of the eye are known. Methods for iris recognition implement pattern-recognition techniques to compare an acquired image of a subject's iris against a previously stored image of the subject's iris, and thereby determine or verify identity of the subject. A digital feature set corresponding to an acquired iris image is encoded based on the image, using mathematical or statistical algorithms. The digital feature set or template is thereafter compared with databases of previously encoded digital templates (stored feature sets corresponding to previously acquired iris images), for locating a match and determining or verifying identity of the subject.
Apparatuses for iris recognition typically comprise an imaging apparatus for capturing an image of the subject's iris(es) and an image processing apparatus for comparing the captured image against previously stored iris image information. The imaging apparatus and image processing apparatus may comprise separate devices, or may be combined within a single device.
While iris recognition apparatuses have been previously available as dedicated or stand alone devices, it is increasingly desirable to incorporate iris recognition capabilities into handheld devices or mobile communication devices or mobile computing devices (collectively referred to as “mobile devices”) having inbuilt cameras, such as for example, mobile phones, smart phones, personal digital assistants, tablets, laptops, or wearable computing devices.
Implementing iris based recognition in mobile devices is convenient and non-invasive and gives individuals access to compact ubiquitous devices capable of acquiring iris images of sufficient quality to enable recognition (identification or verification) of identity of an individual. By incorporating iris imaging apparatuses into mobile devices, such mobile devices achieve biometric recognition capabilities, which capabilities may be put to a variety of uses, including access control for the mobile device itself.
While prior art iris imaging systems are theoretically capable of being incorporated into mobile devices, the time taken by prior art iris image processing systems to process and compare iris image information against previously stored iris information would be significant—leading to evident time lags between iris image acquisition and recognition (or a refusal to recognize).
The primary underlying cause for time lags is that reliable iris image processing and feature extraction is computationally intensive, making it difficult to process every frame within a sequence of image frames. This is particularly the case, for the reason that state-of-the-art image sensors produce at least 30 frames per second in video mode. A further drawback of attempting to compare every frame within a sequence of image frames produced by an image sensor with the stored template(s) is that too many image comparisons may increase the observed false matches. The incidence of false matches is measured in terms of the false match rate (FMR), or the false positive identification rate (FPIR) of the recognition system under observation.
To overcome the above drawbacks, an automatic image selection process may be implemented. The selection method computes one of more “quality” measurements of each image frame and selects the best frame detected within a predetermined timeframe, or alternatively one or more frames that satisfy predetermined quality criteria. Existing commerically available iris recognition systems apply automatic image selection methods as a standard approach to reducing time lags.
A quality assessment criterion in prior art systems is sharpness (also called focus) measurement of the image frame. Focus assessment based image selection algorithms have been found to improve efficiencies of an iris recognition system. Computationally efficient image processing methods are typically used to obtain a scalar value for each frame denoting its focus quality and the image frame that exceeds a predetermined focus threshold is selected for further processing and comparison.
In addition to reducing time lags and conserving processing resources, automatic image selection processes are implemented in applications where reference templates (e.g. iris image feature sets stored in a database) may not be readily available at the location of image capture. Commercial deployments of iris based recognition systems in military, civilian, border control, national ID, police, and surveillance applications typically fall within this category. Such applications require the recognition system to store, transmit or forward the automatically selected image frame, which frame is compared against referenced templates at a later time or at a different location. For example, in a military application, the selected (“captured”) image or extracted feature set may be sent from a foreign country to a central server in home country for comparison. In another example, in a national ID system such as India's UIDAI, the captured image is sent over to a server farm to be de-duplicated against all previously enrolled subjects.
Despite the above, there are disadvantages to using the automatic image selection process—for the reason that a quality measurement algorithms does not always predict an image frame's match-ability accurately enough. For example, it has been found that rejecting 10% lowest quality images in a database only improves the false negative match rate (FNMR) from 10% to 7%. This is acknowledged as presenting a poor trade-off and confirms that quality assessment algorithms are not sufficiently accurate predictors of match-ability.
It is therefore preferable that application of iris based recognition systems within mobile devices, and systems where reference templates are readily available, should not be subjected to the drawbacks that the automatic image selection process imposes. Similarly it is preferable that quality assessment related drawbacks be avoided in certain client-server type iris recognition systems, where reference templates can be pulled from a central server to a local client where the iris imaging occurs.
The present invention seeks to overcome the drawbacks associated with automatic image selection methods, by implementing alternative methods for improving response time and accuracy for iris based recognition systems.